In optical satellite remote sensing sea images, not only the target or object information is contained, but also the seawater background information of the sea surface around the target. The appearance of seawater background in the image may be varied as, for example, storms, surges, waves, vortexes, bubbles, etc. due to various natural factors such as wind strength, wind direction, waves, and ambient humidity or the like on the sea surface. In order to effectively detect the targets in a remote sensing sea image, it is a good way to model and suppress the seawater background.
Currently, there are some major methods for seawater background modeling and suppression of optical satellite remote sensing sea images in the prior. The methods are as follows:
I. Mathematical Morphology-based method. The method uses structural basic-elements to probe spatial repetitiveness domain with similar features, i.e. regions of seawater background, and removes them from the image, so as to extract the target region. When using the Mathematical Morphology method for detection, the result is relevant to the selection of structural basic-elements. However, it has always been crucial and also difficult to select a better structural basic-element.
II. Image Spatial Grayscale Statistics Distribution Model-based method. The method first selects a probability model (such as Gaussian Model, K-Distribution Model, etc.) that best describes the space grayscale statistics distribution feature of sea background of the remote sensing sea image, and then estimates parameters of the distribution model according to the spatial grayscale of sea background, and at the end determines the model probability of gray level of respective pixel in the sea remote sensing image which contains targets by using the spatial grayscale statistics distribution model of seawater background, thereby segments the vessel target regions from the image. In the case of sea background with relative tranquility, the sea clutter may be fitted by selecting a suitable distribution model. However, for an image with complex background clutter, the distribution model is often not well defined, resulting in the bad accuracy of segmentation of target regions.
III. Fractal Model-based method. The method first uses the fractal theory and technique to carry out the multi-scale fractal dimension decomposition of an image, and then segments the sea background region and the target region according to their difference in fractal dimension, thereby detects and extracts the target regions. However, the actual image is prone to be affected by, for example, background complexity, random noise, image quality, etc., it would be difficult to distinguish between the sea background and the target area by only a single scale or constant fractal dimension.
IV. Visual Saliency Model-base method. The method first produces a visual saliency image through feature extraction, saliency calculation and saliency image fusion, and then probes the relatively salient visual objects in the produced saliency image and extracts the corresponding regions so as to complete the detection on the target region. The method introduces multiples of features, which can segment the target regions from the sea background in a better way. However, there is no reliable assessment method to select a proper feature because there are too many features to be selected.
All in all, the existed background model based on Image Spatial Grayscale Distribution has not been able to describe the background clutter of image when the sea background is complex and fit the sea background of the satellite remote sensing sea image well, resulting in high false alarm rate and low detection accuracy of the target detection method of the satellite remote sensing sea image based on Spatial Grayscale Distribution Model.